Generalized machine learning models to predict significant wave height utilizing wind and atmospheric parameters-Reference-Cited by-同舟云学术

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Generalized machine learning models to predict significant wave height utilizing wind and atmospheric parameters

Published:2024-07 Issue: Volume:23 Page:100623
ISSN:2590-1745
Container-title:Energy Conversion and Management: X
language:en
Short-container-title:Energy Conversion and Management: X

Author:

Hasan Abid^ORCID,Kayes Imrul^ORCID,Alam Minhazul,Shahriar Tanvir^ORCID,Ahsan Habib M.

Publisher

Elsevier BV

Reference124 articles.

1. The assessment of extreme wave analysis methods applied to potential marine energy sites using numerical model data;Agarwal;Renew Sustain Energy Rev,2013

2. Soil erosion risk assessment with CORINE model: case study in the Danjiangkou Reservoir region, China;Zhu;Stoch Environ Res Risk A,2012

3. Introduction to the special SERRA issue on “Risks, Uncertainties and the Environment”;Wu;Stoch Environ Res Risk A,2011

4. Fatigue reliability analysis of deep water rigid marine risers associated with Morison-type wave loading;Nazir;Stoch Environ Res Risk A,2008

5. Prediction of extreme wave heights using neuro wavelet technique;Dixit;Appl Ocean Res,2016

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